22 citations · 31 across the 9 of their papers we have counts for
9 papers
The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
David Pape, Jonathan Evertz, Lea Schönherr
Progress in LLMs is increasingly measured through standardized benchmarks, where state-of-the-art improvements are often separated by fractions of a percentage point. At the same t…
No More, No Less: Task Alignment in Terminal Agents
Sina Mavali, David Pape, Jonathan Evertz +5
Terminal agents are increasingly capable of executing complex, long-horizon tasks autonomously from a single user prompt. To do so, they must interpret instructions encountered in…
Unknown Unknowns: Do Hidden Intentions in LLMs Evade Detection?
Devansh Srivastav, David Pape, Lea Schönherr
LLMs expand accessibility and provide wide-reaching access to information. Yet these interactions also create opportunities to embed subtle, goal-oriented behaviours that shape wha…
Chasing Shadows: Pitfalls in LLM Security Research
Jonathan Evertz, Niklas Risse, Nicolai Neuer +12
Large language models (LLMs) are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of re…
Monitoring Robustness and Individual Fairness
Ashutosh Gupta, Thomas A. Henzinger, Konstantin Kueffner +2
Input-output robustness appears in various different forms in the literature, such as robustness of AI models to adversarial or semantic perturbations and individual fairness of AI…
Adversarial Robustness of AI-Generated Image Detectors in the Real World
Sina Mavali, Jonas Ricker, David Pape +2
The rapid advancement of Generative Artificial Intelligence (GenAI) capabilities is accompanied by a concerning rise in its misuse. In particular the generation of credible misinfo…
Prompt Obfuscation for Large Language Models
David Pape, Sina Mavali, Thorsten Eisenhofer +1
System prompts that include detailed instructions to describe the task performed by the underlying LLM can easily transform foundation models into tools and services with minimal o…
On the Limitations of Model Stealing with Uncertainty Quantification Models
David Pape, Sina Däubener, Thorsten Eisenhofer +2
Model stealing aims at inferring a victim model's functionality at a fraction of the original training cost. While the goal is clear, in practice the model's architecture, weight d…
Generating Simple Directed Social Network Graphs for Information Spreading
Christoph Schweimer, Christine Gfrerer, Florian Lugstein +4
Online social networks are a dominant medium in everyday life to stay in contact with friends and to share information. In Twitter, users can connect with other users by following…